Supporting Interactive Text Mining Process With Natural Language and Dialog

ABSTRACT

A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a document analysis device for performing a statistical analysis of documents with respect to a facet. An acceptance module accepts a natural language sentence. An extraction module extracts a first facet from the natural language sentence. A statistical analysis module performs a first statistical analysis of a set of documents with respect to the first facet and determines a value of the first facet based on a result of the first statistical analysis responsive to information being extracted from the natural language sentence, the information requesting for a second statistical analysis. The statistical analysis module performs the second statistical analysis of the set of documents using the value of the first facet. A user interface presents a second facet determined based on a result of the second statistical analysis.

BACKGROUND

The present application relates generally to an improved data processingapparatus and method and more specifically to mechanisms for statisticalanalysis of documents with respect to facet.

Text mining is a technology for acquiring knowledge from a large amountof unstructured text data of documents without necessarily reading theentire content of the documents. A text mining system may analyze theunstructured text data, and extract facets, which are sets of words orphrases representing features of the documents. Further, the text miningsystem may narrow down the documents with queries (e.g., queries innatural language sentence search, queries in facet search), and performvarious statistical analyses of the current documents (the narrowed-downdocuments) regarding the facets.

To acquire significant results of the text mining, one analysis processis insufficient and two analysis processes need to be executed. The twoanalysis processes may include the first analysis process of narrowingdown documents into interesting documents and identifying words specificto the interesting documents, and the second analysis process ofidentifying the cause for appearance of the words.

However, since only the first analysis process is conventionally assumedto be executed, a problem arises that a user is not likely to acquiresignificant results of the text mining.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described herein in the DetailedDescription. This Summary is not intended to identify key factors oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

In one illustrative embodiment, a method is provided in a dataprocessing system comprising at least one processor and at least onememory, the at least one memory comprising instructions executed by theat least one processor to cause the at least one processor to implementa document analysis device for performing a statistical analysis ofdocuments with respect to a facet. The method comprises accepting, by anacceptance module executing within the document analysis device, anatural language sentence. The method further comprises extracting, byan extraction module executing within the document analysis device, afirst facet from the natural language sentence. The method furthercomprises performing, by a statistical analysis module executing withinthe document analysis device, a first statistical analysis of a set ofdocuments with respect to the first facet. The method further comprisesdetermining, by the statistical analysis module, a value of the firstfacet based on a result of the first statistical analysis responsive toinformation being extracted from the natural language sentence, theinformation requesting for a second statistical analysis. The methodfurther comprises performing, by the statistical analysis module, thesecond statistical analysis of the set of documents using the value ofthe first facet. The method further comprises presenting, by a userinterface executed by the data processing system, a second facetdetermined based on a result of the second statistical analysis.

In other illustrative embodiments, a computer program product comprisinga computer useable or readable medium having a computer readable programis provided. The computer readable program, when executed on a computingdevice, causes the computing device to perform various ones of, andcombinations of, the operations outlined above with regard to the methodillustrative embodiment.

In yet another illustrative embodiment, a system/apparatus is provided.The system/apparatus may comprise one or more processors and a memorycoupled to the one or more processors. The memory may compriseinstructions which, when executed by the one or more processors, causethe one or more processors to perform various ones of, and combinationsof, the operations outlined above with regard to the method illustrativeembodiment.

These and other features and advantages of the present invention will bedescribed in, or will become apparent to those of ordinary skill in theart in view of, the following detailed description of the exampleembodiments of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention, as well as a preferred mode of use and further objectivesand advantages thereof, will best be understood by reference to thefollowing detailed description of illustrative embodiments when read inconjunction with the accompanying drawings, wherein:

FIG. 1 depicts an example of actual analysis processes;

FIG. 2 depicts a block diagram of a document analysis system inaccordance an illustrative embodiment;

FIG. 3 depicts an example of an input screen in accordance with anillustrative embodiment;

FIG. 4 depicts an example of a mining graph screen in accordance with anillustrative embodiment;

FIG. 5 depicts an example of a mining graph screen displayed when anautomatic analysis designation is included in a natural languagesentence in accordance with an illustrative embodiment;

FIG. 6 depicts an example of a facet screen displayed in addition to themining graph screen in accordance with an illustrative embodiment;

FIG. 7 depicts an example of the mining graph screen displayedimmediately before a detail analysis screen is called in accordance withan illustrative embodiment;

FIG. 8 depicts an example of a detail analysis screen in accordance withan illustrative embodiment;

FIGS. 9A and 9B depict a flowchart representing an example of anoperation of document analysis in accordance with an illustrativeembodiment;

FIG. 10 depicts a pictorial representation of an example distributeddata processing system in which aspects of the illustrative embodimentsmay be implemented; and

FIG. 11 is a block diagram of just one example data processing system inwhich aspects of the illustrative embodiments may be implemented.

DETAILED DESCRIPTION

The illustrative embodiments provide a system and user interface tosupport for an interactive text mining process with natural languagedialog. The system recognizes the user's analysis and performs automaticanalysis and assistance. Analysis the user wishes to perform can bedescribed in a natural language sentence. The system understands thesteps of analysis from the natural language sentence and assists theanalysis. More specifically, the system automatically performs theanalysis step while appropriately storing required information throughinteraction with the user and displays an analysis screen.

The user interface allows analysis situations, the relationship betweenwords, and analyzed content to be intuitively understood using ananalysis input screen, for displaying natural language sentence inputsand natural language sentence samples, and a mining screen for actualmining. The mining screen is made up of a mining graph screen forvisualizing the interactive mining process, an analysis screen fordisplaying an optimal analysis dashboard from a current set of documentsand the facets to be analyzed, and a facet screen for listing availablefacets.

Before beginning the discussion of the various aspects of theillustrative embodiments, it should first be appreciated that throughoutthis description the term “mechanism” will be used to refer to elementsof the present invention that perform various operations, functions, andthe like. A “mechanism,” as the term is used herein, may be animplementation of the functions or aspects of the illustrativeembodiments in the form of an apparatus, a procedure, or a computerprogram product. In the case of a procedure, the procedure isimplemented by one or more devices, apparatus, computers, dataprocessing systems, or the like. In the case of a computer programproduct, the logic represented by computer code or instructions embodiedin or on the computer program product is executed by one or morehardware devices in order to implement the functionality or perform theoperations associated with the specific “mechanism.” Thus, themechanisms described herein may be implemented as specialized hardware,software executing on general purpose hardware, software instructionsstored on a medium such that the instructions are readily executable byspecialized or general purpose hardware, a procedure or method forexecuting the functions, or a combination of any of the above.

The present description and claims may make use of the terms “a,” “atleast one of,” and “one or more of” with regard to particular featuresand elements of the illustrative embodiments. It should be appreciatedthat these terms and phrases are intended to state that there is atleast one of the particular feature or element present in the particularillustrative embodiment, but that more than one can also be present.That is, these terms/phrases are not intended to limit the descriptionor claims to a single feature/element being present or require that aplurality of such features/elements be present. To the contrary, theseterms/phrases only require at least a single feature/element with thepossibility of a plurality of such features/elements being within thescope of the description and claims.

In addition, it should be appreciated that the following descriptionuses a plurality of various examples for various elements of theillustrative embodiments to further illustrate example implementationsof the illustrative embodiments and to aid in the understanding of themechanisms of the illustrative embodiments. These examples intended tobe non-limiting and are not exhaustive of the various possibilities forimplementing the mechanisms of the illustrative embodiments. It will beapparent to those of ordinary skill in the art in view of the presentdescription that there are many other alternative implementations forthese various elements that may be utilized in addition to, or inreplacement of, the examples provided herein without departing from thespirit and scope of the present invention.

FIG. 1 shows an example of actual analysis processes. In this example, auser is assumed to analyze documents about vehicle failures to find outproblems frequently occurring in a vehicle model (hereinafter referredto simply as a “model”) and causes of the problems.

In the process #1, the user may first narrow down the documents using,as a query word, the model to be analyzed (“ABC” in this example), asindicated by a node 31. That is, the user may perform a facet searchusing a value “ABC” of a facet “Model.” Hereinafter, the value of thefacet is referred to as a “facet value,” Next, the user may select afacet “Component” to be analyzed and perform a correlation analysis ofthe current documents with respect to the facet. In FIG. 1, a result ofthe correlation analysis is assumed to reveal that a facet value “Brake”is highly correlated to the model “ABC” among facet values of the facet“Component,” as indicated by node 32. Thus, the user may further narrowdown the current documents with the facet value “Brake” to find out whybrakes have problems. That is, the user may perform a facet search usingthe facet value “Brake” of the facet “Component.”

In the process #2, the user may narrow down the current documents withthe facet value “Brake,” as stated above regarding the last analysisstep of the process #1. Next, the user may perform a correlationanalysis of the current documents with respect to various facets (e.g.,a noun, a state, and the like). In FIG. 1, a result of the correlationanalysis is assumed to reveal that a facet value “Rust” is highlycorrelated to the facet value “Brake” among facet values of the facet“Noun,” as indicated by node 33. Further, a result of the correlationanalysis is assumed to reveal that a facet value “Northern” is highlycorrelated to the facet value “Brake” among facet values of the facet“State,” as indicated by node 34. This is because salt is sprinkled toprevent road surfaces from being frozen in the northern states, and thismakes brakes rusty.

However, assuming that the aforementioned text mining system is appliedto such analysis processes, it is required to be improved in variousrespects. For example, the aforementioned text mining system is desiredto suggest a facet that can be considered useful if statistical analysisis performed with respect to the facet. Further, since relationshipsbetween words and phrases obtained by narrowing-down and statisticalanalysis are important, the aforementioned text mining system is desiredto enable a user to understand the relationships.

In view of this, the exemplary embodiments may provide a system thatrecognizes a user's intention to analyze the documents, analyzes thedocuments automatically, and assists the user in analyzing thedocuments. The system may. further include a user interface that enablesthe user to intuitively understand analysis situations, relationshipsbetween analyzed words or phrases, and analysis results.

FIG. 2 shows a block diagram of a document analysis system in accordancewith an illustrative embodiment. As shown in the figure, the documentanalysis system may include a document analysis device 10 and a userinterface 20. The document analysis device 10 may recognize a user'sintention to analyze the documents, automatically analyze the documents,and assist the user in analyzing the documents.

That is, the document analysis device 10 may enable the user to describean analysis that the user wishes to perform in a natural languagesentence. The document analysis device 10 may understand analysis stepsfrom the natural language sentence and assist the user in the analysis.More specifically, the document analysis device 10 may perform theanalysis steps automatically while appropriately complementing requiredinformation through interaction with the user and display an analysisscreen.

For example, in the abovementioned example, the user may input a naturallanguage sentence “What Component is highly correlated to Model ABC andWhy?” to the document analysis device 10. The document analysis device10 may understand the natural language sentence and automaticallyexecute process #1. Further, the document analysis device 10 may advancethe processing to the process #2, suggest facets as candidates for ananalysis axis if high correlation is likely to be detected with respectto the facets, and display the facets on the analysis screen.

As shown FIG. 2, the document analysis device 10 may include anacceptance module 11, an extraction module 12, a narrowing down module13, a statistical analysis module 14, a selection module 15, asuggestion module 16, and a detail analysis module 17.

The acceptance module 11 may have predefined patterns of understandablenatural language sentences and may understand a meaning of a givennatural language sentence through pattern matching. The natural languagesentence may basically represent one analysis process of interactivetext mining. Thus, the natural language sentence may include a facet tobe analyzed (hereinafter referred to as an “analysis facet”), a type ofa statistical analysis to he used (hereinafter referred to as a“statistical analysis type”), and, if necessary, a query word or phrasefor narrowing down the documents (hereinafter referred to as a “query”).The analysis facet may be included in the natural language sentence inthe form of the name of the analysis facet. A list of the names ofanalysis facets are assumed to be provided by the user to the system inadvance. Note that the analysis facet included in the natural languagesentence serves as one example of a first facet. The statisticalanalysis type may also be included in the natural language sentence inthe form of the name of the statistical analysis. Note that thestatistical analysis of the type included in the natural languagesentence serves as one example of a first statistical analysis.

If the natural language sentence includes an ambiguous query, theacceptance module 11 may display a screen for allowing the user to solvethe ambiguity. For example, if the natural language sentence includes aquery “ABC,” the acceptance module 11 may display a screen for the userto determine which of a query of a facet “Model,” a query of a facet“Noun,” and a query for simple text search is the query “ABC” includedin the natural language sentence.

In many analyses, one analysis process is insufficient for theinteractive text mining, and two analysis processes are typicallyexecuted. The two analysis processes may include the first analysisprocess of narrowing down documents into interesting documents andidentifying words specific to the interesting documents (correspondingto the process #1 of FIG. 1), and the second analysis process ofidentifying the cause for appearance of the words (corresponding to theprocess #2 of FIG. 1). Thus, in the preferred exemplary embodiment, thenatural language sentence may include a specific word or phrase fordesignating an automatic analysis, Hereinafter, the specific word orphrase for designating an automatic analysis is referred to as an“automatic analysis designation.” The automatic analysis designation maydesignate the system to execute the first analysis process and topresent analysis facets that can be considered useful if statisticalanalysis is performed with respect to the facets in the second analysisprocess. For example, a phrase such as “and Why?” may be used as theautomatic analysis designation.

The extraction module 12 may extract the analysis facet, the statisticalanalysis type, and the query. Further, the extraction module 12 mayextract the automatic analysis designation from the natural languagesentence if it is included in the natural language sentence. Forexample, the extraction module 12 may extract the analysis facet“Component,” the statistical analysis type “Correlation analysis,” andthe query “ABC” of the facet “Model” from the natural language sentence“What Component is highly correlated to Model ABC?” The extractionmodule 12 may extract the analysis facet “Component,” the statisticalanalysis type “Correlation analysis,” the query “ABC” of the facet“Model,” and the automatic analysis designation “and Why?” from thenatural language sentence “What Component is highly correlated to ModelABC, and Why?” The extraction module 12 may extract the analysis facet“Product” and the statistical analysis type “Sentiment analysis” fromthe natural language sentence “What Product has the best sentiment?”

The narrowing down module 13 may narrow down the documents with thequery extracted from the natural language sentence and treat thenarrowed-down documents as the current documents. If no query has beenextracted from the natural language sentence, the narrowing down module13 may treat all the documents as the current documents.

The statistical analysis module 14 is assumed to hold a list ofstatistical analysis types and words or phrases associated with thestatistical analysis types. For example, the statistical analysis module14 may recognize a correlation analysis if a word “correlation” or itsderived word is included in the natural language sentence and recognizea sentiment analysis if a word “sentiment” is included in the naturallanguage sentence. If the automatic analysis designation is notextracted from the natural language sentence, the statistical analysismodule 14 may perform the statistical analysis with respect to theanalysis facet extracted from the natural language sentence and displaya result of the statistical analysis on an analysis screen. If theautomatic analysis designation is extracted from the natural languagesentence, the statistical analysis module 14 may perform the statisticalanalysis with respect to the analysis facet extracted from the naturallanguage sentence and proceed automatically to the next analysisprocess.

The selection module 15 may select a facet value based on the result ofthe statistical analysis using a predefined algorithm and narrow downthe current documents with the selected facet value. Note that somealgorithms can find plural candidates for the facet value, and theselection module 15 may display a screen prompting a user to select oneof them. The selection module 15 may use information in the naturallanguage sentence to select the algorithm. Specifically, the selectionmodule 15 may use an adjective word or phrase, adverbial word or phrase,or the like, which modifies a word or phrase associated with thestatistical analysis type. For example, if a phrase “the highestcorrelation” is included in the natural language sentence, the selectionmodule 15 may select the facet value having the highest correlationindicator based on the result of the correlation analysis. If a phrase“highly correlated” is included in the natural language sentence, theselection module 15 may obtain facet values having the top threecorrelation indicators and present the facet values to the user. If aphrase “empirically correlated” is included in the natural languagesentence, the selection module 15 may select the facet value which isempirically significant based on a result of software processing (e.g.,machine learning of the past statistical analysis).

The suggestion module 16 may perform a statistical analysis of thecurrent documents with respect to facets. The statistical analysis maybe a default statistical analysis defined by the system. Further, thestatistical analysis may be performed with respect to each of the facetsdefined by the system. The suggestion module 16 may present analysisfacets, each of which includes many facet values having high statisticalindicators, as analysis axes. Although assumed to perform thestatistical analysis through a simple brute force algorithm, namely withrespect to each of all the facets defined by the system, the suggestionmodule 16 may use another algorithm. Note that the statistical analysisperformed by the suggestion module 16 serves as one example of a secondstatistical analysis, and an analysis facet presented by the suggestionmodule 16 serves as one example of a second facet.

The detail analysis module 17 may display a detailed result of thestatistical analysis on a detail analysis screen. The analysis processmay proceed to the third analysis process, the fourth analysis process,and so on by further narrowing down the current documents with a facetvalue selected on the detail analysis screen. In this case, the detailanalysis module 17 may cause the selection module 15 and the suggestionmodule 16 to execute the same processing as in the second analysisprocess.

The user interface 20 may enable a user to intuitively understandanalysis situations, relationships between analyzed words or phrases,and analysis results. As shown FIG. 2, the user interface 20 may includean input screen 21 and a mining screen 22. The input screen 21 maydisplay the natural language sentence inputted by a user and naturalsentence samples stored in the system in advance. The input screen 21may be displayed as an initial screen, and changed to the mining screen22 in response to designation of analysis through a natural languagesentence.

The mining screen 22 may be operated for actual mining. The miningscreen 22 may include a mining graph screen 23, a facet screen 24 and adetail analysis screen 25. The mining graph screen 23 may display mininggraphs for visualizing interactive text mining processes. The facetscreen 24 may display a list of available facets. The detail analysisscreen 25 may display a dashboard obtained from the current documentsand the analysis facets. Although the mining screen 22 initially has alayout as shown in FIG. 2, for example, the layout may be changed.

FIG. 3 shows an example of the input screen 21 in accordance with anillustrative embodiment. As shown in the figure, the input screen 21 mayinclude an input area 211, and sample display areas 212 a to 212 c. Theuser may start analysis by inputting a natural language sentence to theinput area 211, or by selecting a natural language sentence sampledisplayed in any one of the sample display areas 212 a to 212 c. Whenthe user inputs the natural language sentence to the input area 211, thenatural language sentence may be verified against sentence patterns heldby the system, and natural language sentence samples corresponding tomatched sentence patterns may be displayed in the sample display areas212 a to 212 c as candidates for the natural language sentence. The usermay select one natural language sentence sample from among thecandidates. When the input area 211 becomes void, an initial list of thenatural language sentence samples may be displayed in the sample displayareas 212 a to 212 c. Although the natural language sentence is assumedto be displayed basically in text as it is on the input screen 21,specific keywords, such as names of facets, may be visually highlighted.Further, a user interface may be provided on which a word representingthe name of a facet or the name of a statistical analysis can simply bechanged into another word.

FIG. 4 shows an example of the mining graph screen 23 in accordance withan illustrative embodiment. As shown in the figure, the mining graphscreen 23 may include a console 231 and a mining tree 232. The console231 may issue notification on the current processing situations of thesystem. The mining tree 232 may visualize the current analysissituations. Upon selection of one of the natural language sentencesamples on the input screen 21 of FIG. 3, this mining graph screen 23may he displayed. The content of the console 231 and the mining tree 232may be changed every time the analysis process changes.

For example, the mining tree 232 of FIG. 4 is assumed to be displayedwhen the natural language sentence “What Component is highly correlatedto Model ABC and Why?” is selected. The mining tree 232 may includenodes 233 a and 233 b each indicating an analysis step with a query usedat the step, and nodes 233 c to 233 e each indicating an analysis stepwith a facet value suggested at the step. Additional information such asthe number of documents, a statistical indicator, a statistical analysistype, or the like may be displayed in association with each of the nodes233 a to 233 e.

The mining tree 232 may include a link 234 b between the nodes 233 a and233 b. This link 234 b is illustrated with a solid line to indicate thatthe documents have already been narrowed down with the querycorresponding to the node 233 b. The mining tree 232 may further includelinks 234 c to 234 e between the node 233 b and the nodes 233 c to 233e, respectively. These links 234 c to 234 e are illustrated with brokenlines to indicate that the current documents are being analyzed withrespect to facets, and the facets are presented as analysis facets eachwith a facet value having a high statistical indicator. Although assumedto be suggested by the system in the default case, the analysis facetsmay be designated by a user, or be replaced with an existing one.

The mining tree 232 of FIG. 4 indicates that a user has narrowed downthe documents with a query “ABC” of a facet “Model” and has furthernarrowed down the current documents with a facet value “Frame” of afacet “Component.” The mining tree 232 of FIG. 4 indicates that thecurrent documents are being analyzed and analysis facets “Negative,”“State,” and “Model Year” are suggested. Note that although only onefacet is assumed to be selected at the analysis step indicated by thenode 233 b in FIG. 4, plural facets may be selected at the step.Further, although only one facet value is assumed to be selected at theanalysis step indicated by the node 233 b in FIG. 4, plural facet valuesmay be selected at the step.

The content of analysis may be changed on the mining graph screen 23.The user may be allowed to easily identify another value of the node byselecting the node. A user interface, such as a pop-up window, may beused to identify another value of the node. By changing the query whichhas already been used to narrow down the documents, the processing maybe branched off to a new analysis process. For example, the facet value“Frame” of the facet “Component” may be changed to a facet value “Brake”of the facet “Component,” and a new analysis process may be started. Inthis case, a new link may be established from the facet value “ABC” ofthe facet “Model,” and an analysis process corresponding to the link maybe treated as a new analysis process.

FIG. 5 shows an example of the mining graph screen 23 displayed when theautomatic analysis designation is included in the natural languagesentence. The first analysis process may be automatically executed. Ifthe automatic analysis designation is included in the natural language,the second analysis process may be basically automatically executed.However, some algorithms used by the system require a user to select oneof plural facet values. In such cases, simple display of an analysisresult may be presented around a node to allow the user's selection. Forexample, FIG. 5 shows a tooltip 235 for prompting a user to select onefacet value from the top three facet values, in the situation Where thenatural language sentence “What Component is highly correlated to ModelABC and Why?” has been selected.

FIG. 6 shows an example of the facet screen 24 displayed in addition tothe mining graph screen 23 in accordance with an illustrativeembodiment. On the facet screen 24, a list of facets may be displayed.If the facets configure tree structures, the tree structures may bedisplayed. The facet screen 24 may provide a new facet to be added toalready-displayed analysis facets on the mining graph screen 23 by adrag-and-drop operation. For example, in FIG. 6, an analysis facet 233 fis added to the analysis facets 233 c to 233 e by a drag-and-dropoperation, as indicated by an arrow 236. Alternatively, the facet screen24 may provide a new facet with which an already-displayed analysisfacet is to be replaced on the mining graph screen 23. Thealready-displayed analysis facet may be replaced with the new facet byoverlaying it on the already-displayed analysis facet by a drag-and-dropoperation. Note that such operation may be performed when the user feelsthat a suggested analysis facet is not useful or wishes to analyze afreely selected facet. Thus, a node representing the new facet may bedisplayed on the mining graph screen 23 with a facet value of the newfacet having a high statistical indicator, as with the already-displayedanalysis facets.

Referring to FIG. 7, there is shown an example of the mining graphscreen 23 displayed immediately before the detail analysis screen 25 iscalled in accordance with an illustrative embodiment. The detailanalysis screen 25 may be displayed when the user selects by a clickoperation one or more nodes representing one or more analysis facets tobe analyzed in detail and activates a trigger for transitioning todetailed analysis of the one or more analysis facets. In FIG. 7, abutton 237 for opening the detail analysis screen 25 is assumed to bedisplayed in a state when the nodes 233 c to 233 f are selected asindicated by thick circular lines. Note that immediately after theanalysis of the documents in response to the natural language sentence,the analysis facets represented by the nodes at the rightmost of themining tree 232 may be automatically selected and displayed on thedetail analysis screen 25.

Alternatively, the detail analysis screen 25 may be displayed when theuser selects by a click operation one or more facet values of the one ormore analysis facets, although this case is not shown in the figure. Inthis case, the current documents may be narrowed down with the selectedone or more facet values, prior to the display of the detail analysisscreen 25. For example, assuming that the facet value “Hole” of theanalysis facet “Negative” is selected, the current documents may benarrowed down with the facet value “Hole,” and subsequently the detailanalysis screen 25 may he displayed.

FIG. 8 shows an example of the detail analysis screen 25 in accordancewith an illustrative embodiment. The detail analysis screen 25 may be adashboard on which a result of statistical analysis regarding theselected analysis facets is displayed. In FIG. 8, a word set 251, a bargraph 252, a circle graph 253, and a map 254 are displayed. For example,the map 254 may be used to display the result of statistical analysisregarding the analysis facets indicating a place name. Note that it isassumed that the system provides a certain rule associating analysisfacets with display modes of the result of statistical analysisregarding the analysis facets.

The analysis facet may be changed by dragging and dropping a facet fromthe facet screen 24 to this detail analysis screen 25. Further, thestatistical analysis type may be optionally changed through an arbitraryuser interface.

If the user wishes to further analyze the current documents with respectto a facet value displayed on the detail analysis screen 25, the usercan proceed to the next analysis step by selecting the facet value andnarrowing down the current documents. Thus, the system may update themining tree 232, and display one or more facets as the next analysisaxis.

In FIG. 2, the mining screen 22 is assumed to be divided to show thedetail analysis screen 25 together with the mining graph screen 23.However, the detail analysis screen 25 may be displayed in variousdisplay modes. For example, the detail analysis screen 25 may bedisplayed on the mining graph screen 23 as a dialog box. Alternatively,the detail analysis screen 25 may be displayed so that the mining graphscreen 23 is changed to the detail analysis screen 25.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an object.oriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement, thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

FIGS. 9A and 9B show a flowchart representing an example of theoperation of the document analysis device 10 in accordance with anillustrative embodiment. Note that the documents to be analyzed areassumed to be stored in the storage of the document analysis device 10;however, the documents may be stored remotely from the document analysisdevice 10 depending on the implementation of the illustrativeembodiment.

As shown in FIG. 9A, the acceptance module 11 may display naturallanguage sentence samples in the sample display areas 212 a to 212 c ofthe input screen 21 (step 101). Then, the acceptance module 11 maydetermine whether or not a natural language sentence has been newlyinputted in the input area 211 of the input screen 21 (step 102). If thenatural language sentence has been newly inputted in the input area 211,the acceptance module 11 may further determine whether or not thenatural language sentence has ambiguity (step 103). In particular, theacceptance module 11 may determine whether or not a query in the naturallanguage sentence has ambiguity. If the natural language sentence hasambiguity, the acceptance module 11 may solve the ambiguity on aninteraction screen (step 104), and return the processing to step 101with the natural language sentence samples being updated based on theinputted natural language sentence. On the other hand, if, at step 103,the natural language sentence does not have ambiguity, the acceptancemodule 11 may return the processing to step 101 with the naturallanguage sentence samples being updated based on the inputted naturallanguage sentence without solving any ambiguity.

Meanwhile, if, at step 102, no natural language sentence has been newlyinputted in the input area 211, the acceptance module 11 may determinewhether or not a natural language sentence has been selected from pluralnatural language sentence samples displayed in the sample display areas212 a to 212 c of the input screen 21 (step 105). If no natural languagesentence has been selected, the acceptance module 11 may return theprocessing to step 101.

On the other hand, if, at step 105, a natural language sentence has beenselected, the extraction module 11 may extract an analysis facet, astatistical analysis type, a query, and an automatic analysisdesignation from the natural language sentence (step 106). Note that theextraction module 11 may extract the automatic analysis designation ifit is included in the natural language sentence. Then, the extractionmodule 11 may change the input screen 21 to the mining screen 22 (step107).

Next, the document analysis device 10 may execute the first analysisprocess (step 108). Specifically, the narrowing down module 13 maynarrow down the documents with the query extracted from the naturallanguage sentence. Then, the statistical analysis module 14 may performa statistical analysis of the type extracted from the natural languagesentence and display a part of a mining tree 232 corresponding to thefirst analysis process on the mining graph screen 23.

Subsequently, as shown in FIG. 9B, the selection module 15 may determinewhether or not the automatic analysis designation has been extractedfrom the natural language sentence (step 151). If the automatic analysisdesignation has been extracted from the natural language sentence, theselection module 15 may execute an automatic analysis algorithm (step152). This automatic analysis algorithm may be determined based on aspecific word or phrase modifying the statistical analysis type in thenatural language sentence. Then, the selection module 15 may determinewhether or not the automatic analysis algorithm requires user selection(step 153). If the automatic analysis algorithm requires user selection,the selection module 15 may display an additional screen for the userselection (step 154). For example, the additional screen may includeplural facet values of the analysis facet extracted from the naturallanguage sentence. In response to selection of a facet value by a user,the selection module 15 may narrow down the current documents with theselected facet value (step 155). lf, at step 153, the automatic analysisalgorithm does not require user selection, the selection module 15 maynarrow down the current documents with the facet value selected byitself without requiring user selection in step 155.

Next, the suggestion module 16 may select at least one analysis facet tobe suggested (step 156). Specifically, the suggestion module 16 mayperform a predetermined statistical analysis of the current documentswith respect to each of the facets prepared by the document analysisdevice 10. Then, the suggestion module 16 may select at least one faceteach of which includes many facet values having high statisticalindicators. After that, the suggestion module 16 may update the miningtree 232 on the mining graph screen 23 (step 157). Returning to step151, if the automatic analysis designation has not been extracted fromthe natural language sentence, operation proceeds to step 157 where thesuggestion module 16 may update the mining tree 232 on the mining graphscreen 23.

In this state, various operations are made to the mining tree 232. Thus,the acceptance module 11 may determine whether or not the selectedanalysis facet has been changed (step 158). Specifically, the acceptancemodule 11 may determine whether or not a new analysis facet has beenselected on the facet screen 24 by the user instead of the analysisfacet selected at step 156. If the selected analysis facet has beenchanged, the acceptance module 11 may return the processing to step 157.If the selected analysis facet has not been changed at step 158, theacceptance module 11 may further determine whether or not the selectedfacet value has been changed (step 159). Specifically, the acceptancemodule 11 may determine whether or not a new facet value has beenselected on the mining tree 232 by the user instead of the facet valueselected at step 154. If the selected facet value has been changed, theacceptance module 11 may return the processing to step 155. If, at step159, the selected facet value has not been changed, the acceptancemodule 11 may advance the processing to step 160.

That is, the detail analysis module 17 may display a detailed analysisresult on the detail analysis screen 25 (step 160). For example, thedetailed analysis module 17 may display the detailed analysis result inresponse to a click operation of a button on the detail analysis screen25. Alternatively, the detail analysis module 17 may display thedetailed analysis result in response to a click operation of one or morefacet values of the suggested one or more analysis facets. In this case,the current documents may be narrowed down with the one or more facetvalues, prior to the display of the detailed analysis result on thedetail analysis screen 25.

Also in this state, various operations are made to the detailed analysisresult. Thus, the acceptance module 11 may determine whether or not thefacet value has been selected (step 161). Specifically, the acceptancemodule 11 may determine whether or not a new facet value has beenselected on the detail analysis screen 25 by the user instead of thefacet value selected at step 154. If the facet value has been selected,the acceptance module 11 may return the processing to step 155. If, atstep 161, the facet value has not been selected, the acceptance module11 may end the processing.

In the first alternative exemplary embodiment, the natural languagesentence is assumed to include no specific word or phrase modifying thestatistical analysis type. In this case, the selection module 15 mayexecute a default automatic analysis algorithm defined by the system.For example, the selection module 15 may select the facet value havingthe highest correlation indicator based on the result of the correlationanalysis. Alternatively, the selection module 15 may obtain facet valueshaving the top three correlation indicators and present the facet valuesto the user. Further, the selection module 15 may select the facet valuewhich is empirically significant based on a result of softwareprocessing (e.g., machine learning of the past statistical analysis)).

Next, the second alternative exemplary embodiment will be described. Inthe second alternative exemplary embodiment, the suggestion module 16 isassumed to perform a statistical analysis other than the defaultstatistical analysis defined by the system. For example, the suggestionmodule 16 may perform a statistical analysis of a type selected fromplural types based on results of statistical analyses of the pluraltypes. Alternatively, the suggestion module 16 may perform a statisticalanalysis of the same type as the statistical analysis type extractedfrom the natural language sentence.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The illustrative embodiments may be utilized in many different types ofdata processing environments, In order to provide a context for thedescription of the specific elements and functionality of theillustrative embodiments, FIGS. 10 and 11 are provided hereafter asexample environments in which aspects of the illustrative embodimentsmay be implemented. It should be appreciated that FIGS. 10 and 11 areonly examples and are not intended to assert or imply any limitationwith regard to the environments in which aspects or embodiments of thepresent invention may be implemented. Many modifications to the depictedenvironments may be made without departing from the spirit and scope ofthe present invention,

FIG. 10 depicts a pictorial representation of an example distributeddata processing system in which aspects of the illustrative embodimentsmay be implemented. Distributed data processing system 1000 may includea network of computers in which aspects of the illustrative embodimentsmay be implemented. The distributed data processing system 1000 containsat least one network 1002, which is the medium used to providecommunication links between various devices and computers connectedtogether within distributed data processing system 1000. The network1002 may include connections, such as wire, wireless communicationlinks, or fiber optic cables.

In the depicted example, server 1004 and server 1006 are connected tonetwork 1002 along with storage unit 1008. In addition, clients 1010,1012, and 1014 are also connected to network 1002. These clients 1010,1012, and 1014 may be, for example, personal computers, networkcomputers, or the like. In the depicted example, server 1004 providesdata, such as boot files, operating system images, and applications tothe clients 1010, 1012, and 1014. Clients 1010, 1012, and 1014 areclients to server 1004 in the depicted example, Distributed dataprocessing system 1000 may include additional servers, clients, andother devices not shown.

In the depicted example, distributed data processing system 1000 is theInternet with network 1002 representing a worldwide collection ofnetworks and gateways that use the Transmission ControlProtocol/Internet Protocol (TCP/IP) suite of protocols to communicatewith one another. At the heart of the Internet is a backbone ofhigh-speed data communication lines between major nodes or hostcomputers, consisting of thousands of commercial, governmental,educational and other computer systems that route data and messages. Ofcourse, the distributed data processing system 1000 may also beimplemented to include a number of different types of networks, such asfor example, an intranet, a local area network (LAN), a wide areanetwork (WAN), or the like. As stated above, FIG. 10 is intended as anexample, not as an architectural limitation for different embodiments ofthe present invention, and therefore, the particular elements shown inFIG. 10 should not be considered limiting with regard to theenvironments in which the illustrative embodiments of the presentinvention may be implemented.

As shown in FIG. 10, one or more of the computing devices, e.g., server104, may be specifically configured to implement a system and userinterface to support the interactive text mining process with naturallanguage dialogue. The configuring of the computing device may comprisethe providing of application specific hardware, firmware, or the like tofacilitate the performance of the operations and generation of theoutputs described herein with regard to the illustrative embodiments.The configuring of the computing device may also, or alternatively,comprise the providing of software applications stored in one or morestorage devices and loaded into memory of a computing device, such asserver 104, for causing one or more hardware processors of the computingdevice to execute the software applications that configure theprocessors to perform the operations and generate the outputs describedherein with regard to the illustrative embodiments. Moreover, anycombination of application specific hardware, firmware, softwareapplications executed on hardware, or the like, may be used withoutdeparting from the spirit and scope of the illustrative embodiments.

It should be appreciated that once the computing device is configured inone of these ways, the computing device becomes a specialized computingdevice specifically configured to implement the mechanisms of theillustrative embodiments and is not a general purpose computing device.Moreover, as described hereafter, the implementation of the mechanismsof the illustrative embodiments improves the functionality of thecomputing device and provides a useful and concrete result thatfacilitates interactive text mining with natural language dialogue.

As noted above, the mechanisms of the illustrative embodiments utilizespecifically configured computing devices, or data processing systems,to perform the operations for supporting interactive text miningprocesses with natural language dialogue. These computing devices, ordata processing systems, may comprise various hardware elements whichare specifically configured, either through hardware configuration,software configuration, or a combination of hardware and softwareconfiguration, to implement. one or more of the systems/subsystemsdescribed herein. FIG. 11 is a block diagram of just one example of adata processing system in which aspects of the illustrative embodimentsmay be implemented. Data processing system 1100 is an example of acomputer, such as server 1004 in FIG. 10, in which computer usable codeor instructions implementing the processes and aspects of theillustrative embodiments of the present invention may be located and/orexecuted so as to achieve the operation, output, and external effects ofthe illustrative embodiments as described herein.

In the depicted example, data processing system 1100 employs a hubarchitecture including north bridge and memory controller hub (NB/MCH)1102 and south bridge and input/output (I/O) controller huh (SB/ICH)1104. Processing unit 1106, main memory 1108, and graphics processor1110 are connected to NB/MCH 1102. Graphics processor 1110 may beconnected to NB/MCH 1102 through an accelerated graphics port (AGP).

In the depicted example, local area network (LAN) adapter 1112 connectsto SB/ICH 1104. Audio adapter 1116, keyboard and mouse adapter 1120,modem 1122, read only memory (ROM) 1124, hard disk drive (HDD) 1126,CD-ROM drive 1130, universal serial bus (USB) ports and othercommunication ports 1132, and PCI/PCIe devices 1134 connect to SB/ICH1104 through bus 1138 and bus 1140. PCI/PCIe devices may include, forexample, Ethernet adapters, add-in cards, and PC cards for notebookcomputers. PCI uses a card bus controller, while PCIe does not. ROM 1124may be, for example, a flash basic input/output system (BIOS).

HDD 1126 and CD-ROM drive 1130 connect to SB/ICH 1104 through bus 1140.HDD 1126 and CD-ROM drive 1130 may use, for example, an integrated driveelectronics (IDE) or serial advanced technology attachment (SATA)interface. Super I/O (SIO) device 1136 may be connected to SB/ICH 1104.

An operating system runs on processing unit 1106. The operating systemcoordinates and provides control of various components within the dataprocessing system 1100 in FIG. 11. As a client, the operating system maybe a commercially available operating system such as Microsoft® Windows7®. An object-oriented programming system, such as the Java™ programmingsystem, may run in conjunction with the operating system and providescalls to the operating system from Java™ programs or applicationsexecuting on data processing system 1100.

As a server, data processing system 1100 may be, for example, an IBMeServer™ System p® computer system, Power™ processor based computersystem, or the like, running the Advanced Interactive Executive (AIX®)operating system or the LINUX® operating system. Data processing system1100 may be a symmetric multiprocessor (SMP) system including aplurality of processors in processing unit 1106. Alternatively, a singleprocessor system may be employed.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs are located on storage devices,such as HDD 1126, and may be loaded into main memory 1108 for executionby processing unit 1106. The processes for illustrative embodiments ofthe present invention may be performed by processing unit 1106 usingcomputer usable program code, which may be located in a memory such as,for example, main memory 1108, ROM 1124, or in one or more peripheraldevices 1126 and 1130, for example.

A bus system, such as bus 1138 or bus 1140 as shown in FIG. 11, may becomprised of one or more buses. Of course, the bus system may beimplemented using any type of communication fabric or architecture thatprovides for a transfer of data between different components or devicesattached to the fabric or architecture. A communication unit, such asmodem 1122 or network adapter 1112 of FIG. 11, may include one or moredevices used to transmit and receive data. A memory may be, for example,main memory 1108, ROM 1124, or a cache such as found in NB/MCH 1102 inFIG. 11.

As mentioned above, in some illustrative embodiments the mechanisms ofthe illustrative embodiments may be implemented as application specifichardware, firmware, or the like, application software stored in astorage device, such as HDD 1126 and loaded into memory, such as mainmemory 1108, for executed by one or more hardware processors, such asprocessing unit 1106, or the like. As such, the computing device shownin FIG. 11 becomes specifically configured to implement the mechanismsof the illustrative embodiments and specifically configured to performthe operations and generate the outputs described hereafter with regardto the mechanisms for supporting interactive text mining with naturallanguage dialogue.

Those of ordinary skill in the art will appreciate that the hardware inFIGS. 10 and 11 may vary depending on the implementation. Other internalhardware or peripheral devices, such as flash memory, equivalentnon-volatile memory, or optical disk drives and the like, may be used inaddition to or in place of the hardware depicted in FIGS. 10 and 11.Also, the processes of the illustrative embodiments may be applied to amultiprocessor data processing system, other than the SMP systemmentioned previously, without departing from the spirit and scope of thepresent invention.

Moreover, the data processing system 1100 may take the form of any of anumber of different data processing systems including client computingdevices, server computing devices, a tablet computer, laptop computer,telephone or other communication device, a personal digital assistant(PDA), or the like. In some illustrative examples, data processingsystem 1100 may be a portable computing device that is configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data, for example. Essentially, dataprocessing system 1100 may be any known or later developed dataprocessing system without architectural

As noted above, it should be appreciated that the illustrativeembodiments may take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment containing both hardwareand software elements. In one example embodiment, the mechanisms of theillustrative embodiments are implemented in software or program code,which includes but is not limited to firmware, resident software,microcode, etc.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a communication bus, such as a system bus,for example. The memory elements can include local memory employedduring actual execution of the program code, hulk storage, and cachememories which provide temporary storage of at least some program codein order to reduce the number of times code must he retrieved from bulkstorage during execution. The memory may be of various types including,but not limited to, ROM, PROM, EPROM, EEPROM, DRAM, SRAM, Flash memory,solid state memory, and the like.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening wired or wireless I/O interfaces and/orcontrollers, or the like. I/O devices may take many different formsother than conventional keyboards, displays, pointing devices, and thelike, such as for example communication devices coupled through wired orwireless connections including, but not limited to, smart phones, tabletcomputers, touch screen devices, voice recognition devices, and thelike. Any known or later developed I/O device is intended to be withinthe scope of the illustrative embodiments.

Network adapters may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Modems, cable modems and Ethernet cards are just a few of thecurrently available types of network adapters for wired communications.Wireless communication based network adapters may also be utilizedincluding, but not limited to, 802.11 a/b/g/n wireless communicationadapters, Bluetooth wireless adapters, and the like. Any known or laterdeveloped network adapters are intended to be within the spirit andscope of the present invention.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the describedembodiments. The embodiment was chosen and described in order to bestexplain the principles of the invention, the practical application, andto enable others of ordinary skill in the art, to understand theinvention for various embodiments with various modifications as aresuited to the particular use contemplated. The terminology used hereinwas chosen to best explain the principles of the embodiments, thepractical application or technical improvement over technologies foundin the marketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

1. A method, in a data processing system comprising at least oneprocessor and at least one memory, the at least one memory comprisinginstructions executed by the at least one processor to cause the atleast one processor to implement a document analysis device forperforming a statistical analysis of documents with respect to a facet,the method comprising: accepting, by an acceptance module executingwithin the document analysis device, a natural language sentence;extracting, by an extraction module executing within the documentanalysis device, a first facet from the natural language sentence;performing, by a statistical analysis module executing within thedocument analysis device, a first statistical analysis of a set ofdocuments with respect to the first facet; determining, by thestatistical analysis module, a value of the first facet based on aresult of the first statistical analysis responsive to information beingextracted from the natural language sentence, the information requestingfor a second statistical analysis; performing, by the statisticalanalysis module, the second statistical analysis of the set of documentsusing the value of the first facet; and presenting, by a user interfaceexecuted by the data processing system, a second facet determined basedon a result of the second statistical analysis.
 2. The method of claim1, wherein extracting the first facet from the natural language sentencecomprises extracting a query word or phrase from the natural languagesentence and wherein performing the first statistical analysis comprisesnarrowing down the set of documents using the query word or phraseextracted from the natural language sentence.
 3. The method of claim 1,wherein extracting the first facet from the natural language sentencecomprises extracting a type of the first statistical analysis from thenatural language sentence and wherein performing the first statisticalanalysis comprises performing the first statistical analysis of the typeextracted from the natural language sentence.
 4. The method of claim 1,wherein extracting the first facet from the natural language sentencecomprises extracting an algorithm for determining the value of the firstfacet and wherein determining the value of the first facet comprisesdetermining the value of the first facet using the algorithm extractedfrom the natural language sentence.
 5. The method of claim 1, whereindetermining the value of the first facet comprises selecting the valueof the first facet from a plurality of values of the first facet, theselected value causing the result of the first statistical analysis tobe highest.
 6. The method of claim 1, wherein determining the value ofthe first facet comprises receiving from a user a selection of the valueof the first facet from a plurality of values of the first facet via theuser interface.
 7. The method of claim 1, wherein determining the valueof the first facet comprises selecting, by a suggestion module executingwithin the document analysis device, the value of the first facet from aplurality of values of the first facet.
 8. The method of claim 1,wherein the second statistical analysis is a statistical analysis of apredetermined type.
 9. The method of claim 1, wherein the secondstatistical analysis is a statistical analysis of a type selected from aplurality of types based on results of statistical analyses of theplurality of types.
 10. The method of claim 1, wherein the secondstatistical analysis is a statistical analysis of a same type as a typeof the first statistical analysis. 11-20. (canceled)